Using Analytics to Measure Campaign Effectiveness

Last click measures are 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 𝗰𝗼𝗿𝗿𝗲𝗰𝘁. A large majority of brands know that last click (and/or MTA) measurement is wrong, but a majority continue to use it as the primary measure of marketing performance. There are typically two main reason why: • 𝗟𝗲𝗴𝗮𝗰𝘆 𝗼𝗳 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 - This is a big challenge and tough to change quickly. I have shared a few methods we use to help with this which is linked in comments. • 𝗔𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝗰𝗹𝗶𝗰𝗸 𝗱𝗮𝘁𝗮 𝗶𝘀 "𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 𝗰𝗼𝗿𝗿𝗲𝗰𝘁" - Many brands assume that while the data is wrong, it is correct enough to optimise towards success. This is unfortunately not true, many of the strongest performance last click channels show the weakest incremental value. And vice versa. On the chart below we map campaign types on Last Click ROAS index (100 = best performing on last click ROAS) and MMM ROAS Index (100 = best performing on MMM ROAS). The first thing you should notice, is that the correlation is weak.  Virtually non existent. But there are some clusters of campaign types: 1. 𝗟𝗼𝘄 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝗭𝗼𝗻𝗲 - Campaigns which look brilliant on last click ROAS but show poor incrementality. These look great on a marketing report, but drive little real value. 2. 𝗚𝗼𝗼𝗱 𝗼𝗻 𝗔𝗹𝗹 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗭𝗼𝗻𝗲 - These look good on Last Click ROAS and look good on MMM ROAS, campaigns which drive clear measurable performance and with strong incrementality. 3. 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁 𝘇𝗼𝗻𝗲 - These are bad on Last Click ROAS and bad on MMM ROAS. These campaigns just don't work, not every test succeeds. 4. 𝗡𝗲𝘃𝗲𝗿 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝗻 𝗟𝗮𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗭𝗼𝗻𝗲 - These look terrible on Last Click ROAS, but actually drive strong modelled incremental performance. These campaigns drive really valuable indirect impact, but the last click measurement can't see their value. Normally on a quadrant chart, the bottom left is the troublesome corner. But here the real issues are in top left and bottom right. Campaigns in bottom left get turned off or changed, because they don't work on any measure. It is a failed test, we learn and move on. Campaigns in the top right get continued investment, and will continue to drive business value. The trouble lives in the top left and the bottom right.  Campaigns in top left get increased investment because the spreadsheet looks good, while they deliver little value. Campaigns in bottom right get turned off, then everyone wonders why overall performance got worse. While everyone's focus is on moving up on the chart, the 𝗿𝗲𝗮𝗹 𝗳𝗼𝗰𝘂𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗹𝗲𝗳𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗿𝗶𝗴𝗵𝘁. It will make your marketing reporting spreadsheet look worse, but make business performance better.

GA4's engagement rate metric is a useful diagnostic but it might set the bar too low for what counts as quality traffic when it comes to ad campaigns. Our paid team has been using what we call a "quality traffic" metric (inspired by Jon Loomer), and it's become one of our go-to tools for campaign troubleshooting. The concept is straightforward: you create a composite event that requires two things to be true at the same time before it fires. Time on page AND scroll depth (or visibility of a key element like your CTA). It requires both elements because time alone doesn't work as someone could leave a tab open while making coffee and scroll depth alone doesn't work because someone could skim through through your page in five seconds. But when both conditions are met we have a much better understanding of how much this person actually looked at what we had to say. My newsletter this week walks you through the full GTM setup, how to deploy it across platforms, and how to tune your thresholds so the metric is more useful for your specific circumstances. It's very beginner friendly as well, so if you don't feel like you're super comfortable with GTM, I promise this will be very accessible for you. #GoogleAds #GA4 #PPC #MetaAds #AnalyticsPlaybook

Incrementality testing is crucial for evaluating the effectiveness of marketing campaigns because it helps marketers determine the true impact of their efforts. Without this testing, it's difficult to know whether observed changes in user behavior or sales were actually caused by the marketing campaign or if they would have occurred naturally. By measuring incrementality, marketers can attribute changes in key metrics directly to their campaign actions and optimize future strategies based on concrete data. In this blog written by the data scientist team from Expedia Group, a detailed guide is shared on how to measure marketing campaign incrementality through geo-testing. Geo-testing allows marketers to split regions into control and treatment groups to observe the true impact of a campaign. The guide breaks the process down into three main stages: - The first stage is pre-testing, where the team determines the appropriate geographical granularity—whether to use states, Designated Market Areas (DMAs), or zip codes. They then strategically select a subset of available regions and assign them to control and treatment groups. It's crucial to validate these selections using statistical tests to ensure that the regions are comparable and the split is sound. - The second stage is the test itself, where the marketing intervention is applied to the treatment group. During this phase, the team must closely monitor business performance, collect data, and address any issues that may arise.  - The third stage is post-test analysis. Rather than immediately measuring the campaign's lift, the team recommends waiting for a "cooldown" period to capture any delayed effects. This waiting period also allows for control and treatment groups to converge again, confirming that the campaign's impact has ended and ensuring the model hasn’t decayed. This structure helps calculate Incremental Return on Advertising spending, answering questions like “How do we measure the sales directly driven by our marketing efforts?” and “Where should we allocate future marketing spend?” The blog serves as a valuable reference for those looking for more technical insights, including software tools used in this process. #datascience #marketing #measurement #incrementality #analysis #experimentation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWKzX8X2 

Two years ago, while working on marketing analytics, I faced a challenge in optimizing ad spend for a digital campaign. The marketing team was running social media ads, but despite high traffic, the conversion rate remained low. Instead of increasing the budget, we turned to SQL and data analysis to identify inefficiencies. Breaking Down the Problem with SQL 1️⃣ Finding the Best & Worst Performing Ads We analyzed click-through rates (CTR) and conversion rates for each ad campaign. SELECT campaign_id, ad_id, COUNT(DISTINCT user_id) AS clicks, COUNT(DISTINCT CASE WHEN purchase = 1 THEN user_id END) AS conversions, COUNT(DISTINCT CASE WHEN purchase = 1 THEN user_id END) * 100.0 / COUNT(DISTINCT user_id) AS conversion_rate FROM ad_clicks GROUP BY campaign_id, ad_id ORDER BY conversion_rate DESC; 🔹 Insight: Some ads had a high CTR but low conversions, meaning they attracted traffic but failed to convert. 2️⃣ Identifying Wasted Ad Spend We checked if ads were targeting low-value customers who rarely made purchases. SELECT ad_id, COUNT(DISTINCT user_id) AS total_clicks, COUNT(DISTINCT CASE WHEN customer_lifetime_value < 50 THEN user_id END) AS low_value_clicks FROM ad_clicks ac JOIN customers c ON ac.user_id = c.customer_id GROUP BY ad_id ORDER BY low_value_clicks DESC; 🔹 Insight: A large portion of the budget was spent on users with low lifetime value, leading to poor ROI. 3️⃣ Finding the Best Audience Segments To optimize targeting, we analyzed which customer segments converted best. SELECT age_group, location, COUNT(DISTINCT user_id) AS total_visitors, COUNT(DISTINCT CASE WHEN purchase = 1 THEN user_id END) AS conversions, ROUND(COUNT(DISTINCT CASE WHEN purchase = 1 THEN user_id END) * 100.0 / COUNT(DISTINCT user_id), 2) AS conversion_rate FROM customer_data GROUP BY age_group, location ORDER BY conversion_rate DESC; 🔹 Insight: The highest converting customers were from specific age groups and cities, which weren’t the primary ad targets. Challenges Faced Data Volume Issues: The dataset contained millions of ad clicks, so I used indexed filtering to improve performance. Attribution Problems: Some users converted days after clicking the ad, so we used attribution modeling instead of last-click conversions. Budget Reallocation Resistance: Marketing teams were hesitant, so we presented data-backed ROI projections. Business Impact ✔ 20% decrease in ad spend waste by cutting low-value audiences. ✔ 15% increase in conversion rate after retargeting the right audience. ✔ Better marketing decisions through data-driven campaign optimization. Key Takeaway: SQL isn’t just for reporting—it helps businesses make smarter marketing decisions and maximize ROI. Have you used SQL to optimize marketing campaigns? Let’s discuss!

The better (and cheaper) alternative to multi-touch attribution 🎯📊 Look, I like multi-touch attribution. It’s a way to nice way to divvy up credit across the multitude of marketing activities needed to get a deal across the finish the line. But good multi-touch attribution is expensive and hard to implement. It can struggle with offline vs online, mobile vs desktop, and impressions vs clicks to name a few. But as a marketing leader it’s your job to help determine if your marketing activities are actually working. Did that new revised homepage work? What about that big Youtube campaign? What about the substantial ABM investment? How about those billboards? Marketing is hard. Stakeholders want answers. Your CEO, your board, your CFO, your CRO … better have some solid data because those questions are coming at the next e-staff or board meeting. So today, I’d like to share a simple yet effective technique I’ve used to help get you those answers. Control groups 🧪📊 What’s a Control Group, and Why Does It Matter? If you’ve ever taken a science class, you’re already familiar with the concept. A control group is the group that doesn’t get the “new thing” you’re testing. It serves as your baseline so you can compare it to the group that does get the new experience. Why is this so important? Because without a control group, it’s hard to know if the results you’re seeing are due to the change you made or something completely unrelated. Maybe a competitor launched a new product, or a major economic event shifted customer behavior. Maybe you ran an event that same week. Without a baseline for comparison, you’re guessing at best. Control groups let you measure the real impact of your marketing initiatives. And the best part? It’s free. No fancy tech required. Real-World Examples of Control Groups in Action Ad Campaigns 🎯 At Slack, we tested campaigns in select cities while using the rest of the U.S. as a control. This helped us measure the lift in awareness, leads, and pipeline. Later, we scaled this approach to national campaigns using the rest of the world as a control. Website Changes 🖥️ At Salesforce, we kept a control group that saw the old homepage while testing a new design. This ensured we could attribute any performance improvements to the change, not external events. ABM Campaigns 🏹 In B2B marketing, ABM is powerful, but how do you prove its impact? Target 50 accounts with ABM and leave 50 as a control group. Then measure conversion rates, deal size, and sales velocity. I love control groups. Anyone else out there using them?

It's about time you know when your Meta Campaign is not working 1- Consistently Poor Results ↳ Metrics like CTR, engagement, or conversions show no improvement despite adjustments. Tip: Audit your campaign's performance. Review pixel setup, landing page experience, and ad frequency. Small tweaks might save the campaign if issues are technical. 2- Irrelevant Targeting ↳ Ads fail to connect with the right audience, leading to low relevance scores and wasted impressions. Tip: Refine your audience using Custom or Lookalike Audiences. Leverage insights from past campaigns to better align with your target demographic. 3- Creative Fatigue ↳ Users ignore ads due to repetitive visuals and messaging. Even refreshed creatives fail to regain attention. Tip: Test entirely new ad formats, such as carousel or video ads, and focus on storytelling that resonates emotionally with your audience. 4- Low ROI ↳ The campaign’s cost outweighs its returns, even after budget adjustments. Tip: Analyze high-performing placements and allocate your budget strategically. If performance remains poor, shift to channels with better potential for ROI. 5- Changed Business Focus" ↳ Campaign goals no longer align with the brand’s evolving objectives or market conditions. Tip: Reassess your broader strategy. Ensure that future campaigns align closely with your updated business goals and utilize A/B testing to validate new directions. Sometimes, the best move is to pause, regroup, and rebuild your campaign strategy. Meta’s dynamic platform rewards adaptability and a data-driven approach.

The best dashboards don’t try to impress you. They just quietly make bad decisions impossible. This dashboard by Anastasiya Kuznetsova is the perfect example. No glitter. No vanity KPIs. Just brutal honesty about how each marketing channel performs, month after month. The Marketing Channels Efficiency dashboard nails what most teams never quite get right: clarity through structure. 1. Cohorts, Not Chaos Instead of lumping everything into one timeline, every metric — ROMI, Revenue, Cost per User — is cohort-based. You instantly see which months actually delivered ROI, and which ones just rode on ad spend momentum. That’s how you separate growth from noise. 2. Full-Spectrum ROMI Total, per Channel, per Cohort, per Month. The breakdown isn’t there for decoration — it tells you why returns move. And the red in the heatmap? That’s rare courage. Most dashboards hide bad months. This one wears them like battle scars. 3. Cost per User That Actually Means Something No blended CAC. No made-up marketing math. Just raw Cost per Acquired User, tracked monthly. Simple. Brutal. Effective. 4. Cumulative Table That Teaches Patience Green = Payback. Red = Loss. You can watch how each cohort recovers over time — where campaigns truly compound and where they die early. It’s the kind of insight that humbles even experienced marketers. 5. Clean Hierarchy = Calm Brain Top row = Big Picture. Middle = Channel Efficiency. Bottom = Long-Term Behavior. It reads like a story, not a spreadsheet. And that’s exactly what good analytics should do — narrate performance, not decorate it. If you build dashboards for marketing teams — cohort everything, show losses, tell one clear story per view. That’s how you go from reporting to strategy. Beautifully done, Anastasiya — dashboards like this make analytics look like craftsmanship again. If you just stole at least one idea, give it a repost so others can too. And follow me Nick for more breakdowns of dashboards, metrics, and data strategy — without the fluff.